ArticleAnnals of surgical oncology2026
Development and Validation of a Pathomics-Based Prognostic Model for Patients with Lung Adenocarcinoma Undergoing First-Line EGFR-TKI Therapy.
Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
backgroundAccurate prognostic prediction is crucial for personalized treatment of patients with lung adenocarcinoma (LUAD) receiving epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). This study aims to develop and validate a pathomics-based prognostic model for EGFR-TKI-treated patients with LUAD. PATIENTS AND
methodsData from 122 patients with LUAD who underwent first-line EGFR-TKI therapy were retrospectively analyzed. Pretreatment whole-slide images of hematoxylin and eosin (H&E)-stained biopsy specimens were collected for annotation and feature extraction. Maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) Cox regression were applied to select features associated with disease progression. The selected features were used to construct the pathomicsScore, and its clinical relevance was assessed via Kaplan-Meier analysis. A predictive model incorporating both pathomicsScore and clinical risk factors was developed.
resultsFive pathomics features associated with disease progression were identified, and a pathomicsScore was developed to stratify patients into low- and high-risk groups. PFS analysis revealed longer survival in the low-risk group. Both pathomicsScore and pathological stage were independent predictors of disease progression and were integrated into a predictive model. The model achieved area under the curve (AUCs) of 0.789 and 0.728, sensitivity of 0.909 and 1, and specificity of 0.677 and 0.714 in the training and validation cohorts. Time-dependent receiver operating characteristic (ROC) curves at 6, 12, and 18 months validated the model's predictive performance. Calibration curves showed excellent agreement between predicted and observed progression probabilities. Decision curve analysis confirmed the clinical utility of the model.
conclusionsThe pathomics-based model effectively predicts disease progression in patients with LUAD receiving EGFR-TKI therapy, enabling personalized treatment strategies.
Indexed as
Identifiers
40911243What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.